import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score
def plot_metrics(history, y_true, y_pred_proba):
# Plot accuracy
plt.figure(figsize=(18, 5))
# Plot accuracy
plt.subplot(1, 4, 1)
plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
# Plot loss per epoch
plt.subplot(1, 4, 2)
train_loss = history.history['loss']
val_loss = history.history['val_loss']
plt.plot(np.arange(1, len(train_loss) + 1), train_loss, label='Train Loss')
plt.plot(np.arange(1, len(val_loss) + 1), val_loss, label='Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Loss per Epoch')
plt.legend()
# Calculate and print metrics
accuracy = accuracy_score(y_true, y_pred_proba.round())
precision = precision_score(y_true, y_pred_proba.round())
recall = recall_score(y_true, y_pred_proba.round())
f1 = f1_score(y_true, y_pred_proba.round())
print("Accuracy: ", accuracy)
print("Precision: ", precision)
print("Recall: ", recall)
print("F1 score: ", f1)
# Plot precision, recall, and F1 score
plt.subplot(1, 4, 3)
metrics_names = ['Precision', 'Recall', 'F1 Score']
metrics = [precision, recall, f1]
plt.bar(metrics_names, metrics)
plt.title('Precision, Recall, and F1 Score')
plt.ylabel('Score')
# Plot ROC-AUC curve
plt.subplot(1, 4, 4)
fpr, tpr, thresholds = roc_curve(y_true, y_pred_proba)
roc_auc = roc_auc_score(y_true, y_pred_proba)
plt.plot(fpr, tpr, label=f'ROC-AUC = {roc_auc:.2f}')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC-AUC Curve')
plt.legend(loc='lower right')
plt.tight_layout()
plt.show()
